The test statistic evaluates the size of the difference between two population means relative to the variation of the sample. If equivalence criteria are expressed in terms of a difference between the test mean and reference mean, or a ratio of test mean/reference mean using a lognormal transformation, the t-value measures the difference between the sample reference mean and the sample test mean in units of standard error. If equivalence criteria are expressed in terms of a ratio between the test mean and the reference mean, the t-value measures the difference between the sample test mean and a proportion of the reference mean, relative to the variability of both samples.
You can use the t-value to determine whether to reject the null hypothesis. However, most people use the p-value or the confidence interval because they are easier to interpret.
Generally, the greater the magnitude of difference or ratio relative to the sampling variability, the greater the absolute value of the t-value for the test, and the stronger the evidence against the null hypothesis.
The t-value for each test is used to calculate its corresponding p-value. If the p-value associated with this t-value is less than your significance level, you reject the null hypothesis and conclude that the results are statistically significant. For more information, see the section on the P-value for the test.